Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f0f5f4f3160>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f0f5f3d6080>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.5.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real') 
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, None, name='learning_rate')

    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
alpha = 0.01
leaky_relu = lambda x: tf.maximum(alpha * x, x)

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    with tf.variable_scope('discriminator', reuse=reuse):
        
        
        # Input layer is 32x32x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = leaky_relu(x1)
        # 16x16x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = leaky_relu(bn2)
        # 8x8x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = leaky_relu(bn3)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    with tf.variable_scope('generator', reuse=not is_train):
        
        x1 = tf.layers.dense(z, 7*7*512)
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = leaky_relu(x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = leaky_relu(x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = leaky_relu(x3)
        
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    n_images=16
    _, img_width, img_height, img_channels = data_shape
    
    input_real, z_input, lr = model_inputs(
        img_width, img_height, img_channels, z_dim)
    
    d_loss, g_loss = model_loss(input_real, z_input, img_channels)
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, z_input: batch_z,lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_real: batch_images,z_input: batch_z,lr: learning_rate})
            
                if steps % 50 == 0:
                    train_loss_d = sess.run(d_loss, {z_input: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({z_input: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))    
                    
                    show_generator_output(sess, n_images, z_input, img_channels, data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 64
z_dim = 128
learning_rate = 0.002
beta1 = 0.5



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.0006... Generator Loss: 8.0116
Epoch 1/2... Discriminator Loss: 0.0010... Generator Loss: 8.8379
Epoch 1/2... Discriminator Loss: 0.0001... Generator Loss: 9.4131
Epoch 1/2... Discriminator Loss: 0.0005... Generator Loss: 9.0660
Epoch 1/2... Discriminator Loss: 1.3133... Generator Loss: 4.9172
Epoch 1/2... Discriminator Loss: 0.0067... Generator Loss: 6.1427
Epoch 1/2... Discriminator Loss: 0.0085... Generator Loss: 6.4739
Epoch 1/2... Discriminator Loss: 0.0010... Generator Loss: 7.9412
Epoch 1/2... Discriminator Loss: 0.0013... Generator Loss: 9.0576
Epoch 1/2... Discriminator Loss: 0.0004... Generator Loss: 9.1765
Epoch 1/2... Discriminator Loss: 0.0002... Generator Loss: 9.4517
Epoch 1/2... Discriminator Loss: 0.3422... Generator Loss: 3.3200
Epoch 1/2... Discriminator Loss: 0.0520... Generator Loss: 3.9936
Epoch 1/2... Discriminator Loss: 0.0147... Generator Loss: 5.2303
Epoch 1/2... Discriminator Loss: 0.0015... Generator Loss: 7.3103
Epoch 1/2... Discriminator Loss: 0.1083... Generator Loss: 4.2986
Epoch 1/2... Discriminator Loss: 0.0173... Generator Loss: 4.7377
Epoch 1/2... Discriminator Loss: 0.0719... Generator Loss: 4.0270
Epoch 2/2... Discriminator Loss: 0.0779... Generator Loss: 4.7042
Epoch 2/2... Discriminator Loss: 2.8128... Generator Loss: 0.6030
Epoch 2/2... Discriminator Loss: 0.2428... Generator Loss: 3.9075
Epoch 2/2... Discriminator Loss: 0.2756... Generator Loss: 3.0429
Epoch 2/2... Discriminator Loss: 0.0144... Generator Loss: 6.8924
Epoch 2/2... Discriminator Loss: 1.4659... Generator Loss: 3.1595
Epoch 2/2... Discriminator Loss: 0.4824... Generator Loss: 2.0877
Epoch 2/2... Discriminator Loss: 1.0916... Generator Loss: 3.9750
Epoch 2/2... Discriminator Loss: 0.5748... Generator Loss: 1.4140
Epoch 2/2... Discriminator Loss: 0.9374... Generator Loss: 1.9335
Epoch 2/2... Discriminator Loss: 0.3197... Generator Loss: 2.5745
Epoch 2/2... Discriminator Loss: 0.4466... Generator Loss: 1.8629
Epoch 2/2... Discriminator Loss: 1.2065... Generator Loss: 0.6040
Epoch 2/2... Discriminator Loss: 0.3200... Generator Loss: 3.7987
Epoch 2/2... Discriminator Loss: 0.6990... Generator Loss: 1.5883
Epoch 2/2... Discriminator Loss: 0.0250... Generator Loss: 5.8310
Epoch 2/2... Discriminator Loss: 0.8606... Generator Loss: 0.9221
Epoch 2/2... Discriminator Loss: 0.7444... Generator Loss: 1.5607
Epoch 2/2... Discriminator Loss: 0.8445... Generator Loss: 1.6266

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 64
z_dim = 128
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 5

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/5... Discriminator Loss: 0.0008... Generator Loss: 21.6646
Epoch 1/5... Discriminator Loss: 5.9449... Generator Loss: 0.0044
Epoch 1/5... Discriminator Loss: 0.0098... Generator Loss: 4.8274
Epoch 1/5... Discriminator Loss: 0.0013... Generator Loss: 6.9349
Epoch 1/5... Discriminator Loss: 0.7834... Generator Loss: 1.3515
Epoch 1/5... Discriminator Loss: 0.1566... Generator Loss: 2.7791
Epoch 1/5... Discriminator Loss: 0.3985... Generator Loss: 3.1420
Epoch 1/5... Discriminator Loss: 0.0586... Generator Loss: 4.0681
Epoch 1/5... Discriminator Loss: 1.3226... Generator Loss: 0.7880
Epoch 1/5... Discriminator Loss: 0.2301... Generator Loss: 2.9535
Epoch 1/5... Discriminator Loss: 0.0358... Generator Loss: 3.9388
Epoch 1/5... Discriminator Loss: 0.4953... Generator Loss: 1.5288
Epoch 1/5... Discriminator Loss: 0.2443... Generator Loss: 3.0874
Epoch 1/5... Discriminator Loss: 2.3774... Generator Loss: 0.3995
Epoch 1/5... Discriminator Loss: 1.1742... Generator Loss: 0.6032
Epoch 1/5... Discriminator Loss: 1.3354... Generator Loss: 0.7469
Epoch 1/5... Discriminator Loss: 1.1728... Generator Loss: 0.7903
Epoch 1/5... Discriminator Loss: 1.3836... Generator Loss: 0.7793
Epoch 1/5... Discriminator Loss: 0.9372... Generator Loss: 1.3812
Epoch 1/5... Discriminator Loss: 1.0880... Generator Loss: 0.5777
Epoch 1/5... Discriminator Loss: 1.1514... Generator Loss: 0.8753
Epoch 1/5... Discriminator Loss: 1.5053... Generator Loss: 1.3560
Epoch 1/5... Discriminator Loss: 1.5116... Generator Loss: 0.4856
Epoch 1/5... Discriminator Loss: 1.1011... Generator Loss: 0.7053
Epoch 1/5... Discriminator Loss: 1.2788... Generator Loss: 0.4483
Epoch 1/5... Discriminator Loss: 1.5616... Generator Loss: 1.4326
Epoch 1/5... Discriminator Loss: 1.3943... Generator Loss: 0.5390
Epoch 1/5... Discriminator Loss: 1.2094... Generator Loss: 0.5706
Epoch 1/5... Discriminator Loss: 1.2936... Generator Loss: 0.7513
Epoch 1/5... Discriminator Loss: 1.2981... Generator Loss: 1.7564
Epoch 1/5... Discriminator Loss: 0.5536... Generator Loss: 1.3264
Epoch 1/5... Discriminator Loss: 1.4327... Generator Loss: 0.9472
Epoch 1/5... Discriminator Loss: 1.1916... Generator Loss: 0.9736
Epoch 1/5... Discriminator Loss: 1.0834... Generator Loss: 0.7956
Epoch 1/5... Discriminator Loss: 1.3674... Generator Loss: 0.7142
Epoch 1/5... Discriminator Loss: 0.9920... Generator Loss: 1.6168
Epoch 1/5... Discriminator Loss: 1.1696... Generator Loss: 0.5987
Epoch 1/5... Discriminator Loss: 1.5684... Generator Loss: 0.5018
Epoch 1/5... Discriminator Loss: 1.1159... Generator Loss: 0.7062
Epoch 1/5... Discriminator Loss: 1.5496... Generator Loss: 0.5075
Epoch 1/5... Discriminator Loss: 1.5104... Generator Loss: 1.4346
Epoch 1/5... Discriminator Loss: 0.8533... Generator Loss: 1.0608
Epoch 1/5... Discriminator Loss: 1.1557... Generator Loss: 0.5608
Epoch 1/5... Discriminator Loss: 1.2870... Generator Loss: 0.9369
Epoch 1/5... Discriminator Loss: 1.2345... Generator Loss: 0.7820
Epoch 1/5... Discriminator Loss: 1.4194... Generator Loss: 0.7770
Epoch 1/5... Discriminator Loss: 1.1201... Generator Loss: 1.1457
Epoch 1/5... Discriminator Loss: 1.6937... Generator Loss: 0.3475
Epoch 1/5... Discriminator Loss: 1.5715... Generator Loss: 0.4066
Epoch 1/5... Discriminator Loss: 1.1380... Generator Loss: 0.6898
Epoch 1/5... Discriminator Loss: 1.3517... Generator Loss: 0.7093
Epoch 1/5... Discriminator Loss: 1.4198... Generator Loss: 0.6184
Epoch 1/5... Discriminator Loss: 1.4221... Generator Loss: 0.6509
Epoch 1/5... Discriminator Loss: 0.9045... Generator Loss: 1.5371
Epoch 1/5... Discriminator Loss: 1.3278... Generator Loss: 0.5820
Epoch 1/5... Discriminator Loss: 1.4408... Generator Loss: 0.6344
Epoch 1/5... Discriminator Loss: 1.3878... Generator Loss: 0.6110
Epoch 1/5... Discriminator Loss: 1.4274... Generator Loss: 0.4943
Epoch 1/5... Discriminator Loss: 1.3934... Generator Loss: 0.5272
Epoch 1/5... Discriminator Loss: 1.0311... Generator Loss: 0.9652
Epoch 1/5... Discriminator Loss: 1.2099... Generator Loss: 0.6658
Epoch 1/5... Discriminator Loss: 1.3722... Generator Loss: 0.5373
Epoch 1/5... Discriminator Loss: 1.4093... Generator Loss: 0.6420
Epoch 2/5... Discriminator Loss: 1.5397... Generator Loss: 0.3880
Epoch 2/5... Discriminator Loss: 1.6446... Generator Loss: 0.2721
Epoch 2/5... Discriminator Loss: 1.4638... Generator Loss: 0.4455
Epoch 2/5... Discriminator Loss: 1.3378... Generator Loss: 0.8146
Epoch 2/5... Discriminator Loss: 1.1285... Generator Loss: 0.7730
Epoch 2/5... Discriminator Loss: 1.4476... Generator Loss: 0.4434
Epoch 2/5... Discriminator Loss: 1.2971... Generator Loss: 0.7409
Epoch 2/5... Discriminator Loss: 1.3065... Generator Loss: 0.7714
Epoch 2/5... Discriminator Loss: 1.4216... Generator Loss: 0.4831
Epoch 2/5... Discriminator Loss: 1.3807... Generator Loss: 0.7004
Epoch 2/5... Discriminator Loss: 1.2507... Generator Loss: 0.5629
Epoch 2/5... Discriminator Loss: 1.3456... Generator Loss: 0.5537
Epoch 2/5... Discriminator Loss: 1.3864... Generator Loss: 0.5986
Epoch 2/5... Discriminator Loss: 1.2976... Generator Loss: 0.7243
Epoch 2/5... Discriminator Loss: 1.2194... Generator Loss: 0.7728
Epoch 2/5... Discriminator Loss: 1.4998... Generator Loss: 0.5268
Epoch 2/5... Discriminator Loss: 1.2528... Generator Loss: 0.8066
Epoch 2/5... Discriminator Loss: 1.4043... Generator Loss: 0.5690
Epoch 2/5... Discriminator Loss: 1.4320... Generator Loss: 0.7473
Epoch 2/5... Discriminator Loss: 1.3346... Generator Loss: 0.5394
Epoch 2/5... Discriminator Loss: 1.2923... Generator Loss: 0.6523
Epoch 2/5... Discriminator Loss: 1.3075... Generator Loss: 0.6258
Epoch 2/5... Discriminator Loss: 1.4483... Generator Loss: 0.4772
Epoch 2/5... Discriminator Loss: 1.4122... Generator Loss: 0.6874
Epoch 2/5... Discriminator Loss: 1.4305... Generator Loss: 0.5569
Epoch 2/5... Discriminator Loss: 1.3102... Generator Loss: 0.5589
Epoch 2/5... Discriminator Loss: 1.3831... Generator Loss: 0.6372
Epoch 2/5... Discriminator Loss: 1.4377... Generator Loss: 0.5096
Epoch 2/5... Discriminator Loss: 1.4675... Generator Loss: 0.5432
Epoch 2/5... Discriminator Loss: 1.3673... Generator Loss: 0.6833
Epoch 2/5... Discriminator Loss: 1.4301... Generator Loss: 0.6057
Epoch 2/5... Discriminator Loss: 1.5753... Generator Loss: 0.4830
Epoch 2/5... Discriminator Loss: 1.4788... Generator Loss: 0.4167
Epoch 2/5... Discriminator Loss: 1.4197... Generator Loss: 0.5744
Epoch 2/5... Discriminator Loss: 1.2221... Generator Loss: 0.8315
Epoch 2/5... Discriminator Loss: 1.4584... Generator Loss: 0.4672
Epoch 2/5... Discriminator Loss: 1.3142... Generator Loss: 0.6009
Epoch 2/5... Discriminator Loss: 1.3904... Generator Loss: 0.6420
Epoch 2/5... Discriminator Loss: 1.3270... Generator Loss: 0.5954
Epoch 2/5... Discriminator Loss: 1.4207... Generator Loss: 0.5049
Epoch 2/5... Discriminator Loss: 1.3374... Generator Loss: 0.5935
Epoch 2/5... Discriminator Loss: 1.4300... Generator Loss: 0.5325
Epoch 2/5... Discriminator Loss: 1.3448... Generator Loss: 0.5463
Epoch 2/5... Discriminator Loss: 1.3618... Generator Loss: 0.5593
Epoch 2/5... Discriminator Loss: 1.3965... Generator Loss: 0.6259
Epoch 2/5... Discriminator Loss: 1.3804... Generator Loss: 0.5979
Epoch 2/5... Discriminator Loss: 1.4871... Generator Loss: 0.5557
Epoch 2/5... Discriminator Loss: 1.5933... Generator Loss: 0.4274
Epoch 2/5... Discriminator Loss: 1.3398... Generator Loss: 0.6057
Epoch 2/5... Discriminator Loss: 1.3801... Generator Loss: 0.6730
Epoch 2/5... Discriminator Loss: 1.3662... Generator Loss: 0.6752
Epoch 2/5... Discriminator Loss: 1.4542... Generator Loss: 0.5399
Epoch 2/5... Discriminator Loss: 1.4770... Generator Loss: 0.6216
Epoch 2/5... Discriminator Loss: 1.3932... Generator Loss: 0.7052
Epoch 2/5... Discriminator Loss: 1.4743... Generator Loss: 0.4349
Epoch 2/5... Discriminator Loss: 1.5785... Generator Loss: 0.4492
Epoch 2/5... Discriminator Loss: 1.3283... Generator Loss: 0.8184
Epoch 2/5... Discriminator Loss: 1.4222... Generator Loss: 0.5001
Epoch 2/5... Discriminator Loss: 1.4138... Generator Loss: 0.5208
Epoch 2/5... Discriminator Loss: 1.4309... Generator Loss: 0.4586
Epoch 2/5... Discriminator Loss: 1.4207... Generator Loss: 0.4750
Epoch 2/5... Discriminator Loss: 1.4959... Generator Loss: 0.4249
Epoch 2/5... Discriminator Loss: 1.3127... Generator Loss: 0.5647
Epoch 3/5... Discriminator Loss: 1.4458... Generator Loss: 0.5438
Epoch 3/5... Discriminator Loss: 1.6169... Generator Loss: 0.4449
Epoch 3/5... Discriminator Loss: 1.3907... Generator Loss: 0.5931
Epoch 3/5... Discriminator Loss: 1.4305... Generator Loss: 0.5324
Epoch 3/5... Discriminator Loss: 1.3337... Generator Loss: 0.5877
Epoch 3/5... Discriminator Loss: 1.3470... Generator Loss: 0.6267
Epoch 3/5... Discriminator Loss: 1.5082... Generator Loss: 0.4839
Epoch 3/5... Discriminator Loss: 1.4361... Generator Loss: 0.5475
Epoch 3/5... Discriminator Loss: 1.3534... Generator Loss: 0.7075
Epoch 3/5... Discriminator Loss: 1.5004... Generator Loss: 0.5762
Epoch 3/5... Discriminator Loss: 1.3808... Generator Loss: 0.5699
Epoch 3/5... Discriminator Loss: 1.4373... Generator Loss: 0.4562
Epoch 3/5... Discriminator Loss: 1.3696... Generator Loss: 0.5868
Epoch 3/5... Discriminator Loss: 1.4541... Generator Loss: 0.4618
Epoch 3/5... Discriminator Loss: 1.3407... Generator Loss: 0.5954
Epoch 3/5... Discriminator Loss: 1.4482... Generator Loss: 0.5162
Epoch 3/5... Discriminator Loss: 1.5721... Generator Loss: 0.4257
Epoch 3/5... Discriminator Loss: 1.4824... Generator Loss: 0.4451
Epoch 3/5... Discriminator Loss: 1.8518... Generator Loss: 0.2891
Epoch 3/5... Discriminator Loss: 1.4237... Generator Loss: 0.5927
Epoch 3/5... Discriminator Loss: 1.7841... Generator Loss: 0.3635
Epoch 3/5... Discriminator Loss: 1.4276... Generator Loss: 0.5657
Epoch 3/5... Discriminator Loss: 1.6354... Generator Loss: 0.4489
Epoch 3/5... Discriminator Loss: 1.7319... Generator Loss: 0.3979
Epoch 3/5... Discriminator Loss: 1.4977... Generator Loss: 0.5242
Epoch 3/5... Discriminator Loss: 1.4515... Generator Loss: 0.4980
Epoch 3/5... Discriminator Loss: 1.3523... Generator Loss: 0.5611
Epoch 3/5... Discriminator Loss: 1.5637... Generator Loss: 0.3808
Epoch 3/5... Discriminator Loss: 1.6897... Generator Loss: 0.3940
Epoch 3/5... Discriminator Loss: 1.5518... Generator Loss: 0.4650
Epoch 3/5... Discriminator Loss: 1.4841... Generator Loss: 0.6609
Epoch 3/5... Discriminator Loss: 1.6041... Generator Loss: 0.4564
Epoch 3/5... Discriminator Loss: 1.4673... Generator Loss: 0.5378
Epoch 3/5... Discriminator Loss: 1.5118... Generator Loss: 0.5038
Epoch 3/5... Discriminator Loss: 1.7925... Generator Loss: 0.3796
Epoch 3/5... Discriminator Loss: 1.5248... Generator Loss: 0.4365
Epoch 3/5... Discriminator Loss: 1.3450... Generator Loss: 0.5243
Epoch 3/5... Discriminator Loss: 1.3374... Generator Loss: 0.6983
Epoch 3/5... Discriminator Loss: 1.3947... Generator Loss: 0.5442
Epoch 3/5... Discriminator Loss: 1.3794... Generator Loss: 0.5662
Epoch 3/5... Discriminator Loss: 1.6348... Generator Loss: 0.4029
Epoch 3/5... Discriminator Loss: 1.3023... Generator Loss: 0.4990
Epoch 3/5... Discriminator Loss: 0.0966... Generator Loss: 3.7446
Epoch 3/5... Discriminator Loss: 1.3385... Generator Loss: 0.5311
Epoch 3/5... Discriminator Loss: 1.0552... Generator Loss: 0.7980
Epoch 3/5... Discriminator Loss: 1.3159... Generator Loss: 1.0966
Epoch 3/5... Discriminator Loss: 1.3869... Generator Loss: 0.5494
Epoch 3/5... Discriminator Loss: 1.4842... Generator Loss: 0.4411
Epoch 3/5... Discriminator Loss: 1.6284... Generator Loss: 0.4032
Epoch 3/5... Discriminator Loss: 1.5039... Generator Loss: 0.4275
Epoch 3/5... Discriminator Loss: 1.4002... Generator Loss: 0.4800
Epoch 3/5... Discriminator Loss: 1.2702... Generator Loss: 0.5844
Epoch 3/5... Discriminator Loss: 1.5616... Generator Loss: 0.4603
Epoch 3/5... Discriminator Loss: 1.4109... Generator Loss: 0.4893
Epoch 3/5... Discriminator Loss: 1.2856... Generator Loss: 0.5843
Epoch 3/5... Discriminator Loss: 1.7637... Generator Loss: 0.3543
Epoch 3/5... Discriminator Loss: 1.8101... Generator Loss: 0.2913
Epoch 3/5... Discriminator Loss: 1.6315... Generator Loss: 0.4435
Epoch 3/5... Discriminator Loss: 1.6138... Generator Loss: 0.3884
Epoch 3/5... Discriminator Loss: 1.3447... Generator Loss: 0.5434
Epoch 3/5... Discriminator Loss: 1.5497... Generator Loss: 0.5093
Epoch 3/5... Discriminator Loss: 1.5329... Generator Loss: 0.4387
Epoch 3/5... Discriminator Loss: 1.4762... Generator Loss: 0.4878
Epoch 4/5... Discriminator Loss: 1.5057... Generator Loss: 0.5241
Epoch 4/5... Discriminator Loss: 1.4285... Generator Loss: 0.5211
Epoch 4/5... Discriminator Loss: 1.6295... Generator Loss: 0.5164
Epoch 4/5... Discriminator Loss: 1.4756... Generator Loss: 0.5188
Epoch 4/5... Discriminator Loss: 1.5739... Generator Loss: 0.4361
Epoch 4/5... Discriminator Loss: 1.4942... Generator Loss: 0.5811
Epoch 4/5... Discriminator Loss: 1.5133... Generator Loss: 0.4587
Epoch 4/5... Discriminator Loss: 1.6616... Generator Loss: 0.4365
Epoch 4/5... Discriminator Loss: 1.8356... Generator Loss: 0.3076
Epoch 4/5... Discriminator Loss: 1.4882... Generator Loss: 0.5293
Epoch 4/5... Discriminator Loss: 1.5119... Generator Loss: 0.4894
Epoch 4/5... Discriminator Loss: 1.3899... Generator Loss: 0.4902
Epoch 4/5... Discriminator Loss: 1.6280... Generator Loss: 0.4317
Epoch 4/5... Discriminator Loss: 1.4952... Generator Loss: 0.4417
Epoch 4/5... Discriminator Loss: 1.3047... Generator Loss: 0.5630
Epoch 4/5... Discriminator Loss: 1.3882... Generator Loss: 0.5019
Epoch 4/5... Discriminator Loss: 1.4753... Generator Loss: 0.4595
Epoch 4/5... Discriminator Loss: 1.5937... Generator Loss: 0.4242
Epoch 4/5... Discriminator Loss: 1.4414... Generator Loss: 0.5371
Epoch 4/5... Discriminator Loss: 1.6846... Generator Loss: 0.3862
Epoch 4/5... Discriminator Loss: 1.6055... Generator Loss: 0.4375
Epoch 4/5... Discriminator Loss: 1.5051... Generator Loss: 0.4887
Epoch 4/5... Discriminator Loss: 1.5998... Generator Loss: 0.4095
Epoch 4/5... Discriminator Loss: 1.4583... Generator Loss: 0.4529
Epoch 4/5... Discriminator Loss: 1.7773... Generator Loss: 0.2849
Epoch 4/5... Discriminator Loss: 1.6213... Generator Loss: 0.4097
Epoch 4/5... Discriminator Loss: 1.8735... Generator Loss: 0.3527
Epoch 4/5... Discriminator Loss: 1.5485... Generator Loss: 0.5038
Epoch 4/5... Discriminator Loss: 1.4977... Generator Loss: 0.4953
Epoch 4/5... Discriminator Loss: 1.5696... Generator Loss: 0.4085
Epoch 4/5... Discriminator Loss: 1.4485... Generator Loss: 0.4714
Epoch 4/5... Discriminator Loss: 1.3437... Generator Loss: 0.5217
Epoch 4/5... Discriminator Loss: 1.5035... Generator Loss: 0.4387
Epoch 4/5... Discriminator Loss: 1.4074... Generator Loss: 0.5781
Epoch 4/5... Discriminator Loss: 1.4754... Generator Loss: 0.4716
Epoch 4/5... Discriminator Loss: 1.7071... Generator Loss: 0.3608
Epoch 4/5... Discriminator Loss: 1.5976... Generator Loss: 0.4313
Epoch 4/5... Discriminator Loss: 1.5290... Generator Loss: 0.4293
Epoch 4/5... Discriminator Loss: 1.4780... Generator Loss: 0.4551
Epoch 4/5... Discriminator Loss: 1.5213... Generator Loss: 0.4303
Epoch 4/5... Discriminator Loss: 1.7562... Generator Loss: 0.3975
Epoch 4/5... Discriminator Loss: 1.6631... Generator Loss: 0.4400
Epoch 4/5... Discriminator Loss: 1.6016... Generator Loss: 0.4746
Epoch 4/5... Discriminator Loss: 1.5087... Generator Loss: 0.4305
Epoch 4/5... Discriminator Loss: 1.2401... Generator Loss: 0.5921
Epoch 4/5... Discriminator Loss: 1.5507... Generator Loss: 0.4651
Epoch 4/5... Discriminator Loss: 0.9762... Generator Loss: 0.8295
Epoch 4/5... Discriminator Loss: 1.6464... Generator Loss: 0.3574
Epoch 4/5... Discriminator Loss: 1.6706... Generator Loss: 0.3720
Epoch 4/5... Discriminator Loss: 1.4346... Generator Loss: 0.4904
Epoch 4/5... Discriminator Loss: 1.4709... Generator Loss: 0.4484
Epoch 4/5... Discriminator Loss: 1.6977... Generator Loss: 0.3675
Epoch 4/5... Discriminator Loss: 1.8885... Generator Loss: 0.3183
Epoch 4/5... Discriminator Loss: 1.7995... Generator Loss: 0.3058
Epoch 4/5... Discriminator Loss: 1.3116... Generator Loss: 0.5602
Epoch 4/5... Discriminator Loss: 1.5918... Generator Loss: 0.3703
Epoch 4/5... Discriminator Loss: 1.5676... Generator Loss: 0.5698
Epoch 4/5... Discriminator Loss: 1.5656... Generator Loss: 0.4078
Epoch 4/5... Discriminator Loss: 1.8050... Generator Loss: 0.3173
Epoch 4/5... Discriminator Loss: 1.4335... Generator Loss: 0.5547
Epoch 4/5... Discriminator Loss: 1.5856... Generator Loss: 0.4159
Epoch 4/5... Discriminator Loss: 1.5600... Generator Loss: 0.4087
Epoch 4/5... Discriminator Loss: 1.4374... Generator Loss: 0.4915
Epoch 4/5... Discriminator Loss: 1.5099... Generator Loss: 0.5182
Epoch 5/5... Discriminator Loss: 1.5318... Generator Loss: 0.4158
Epoch 5/5... Discriminator Loss: 1.5403... Generator Loss: 0.3967
Epoch 5/5... Discriminator Loss: 1.5514... Generator Loss: 0.4873
Epoch 5/5... Discriminator Loss: 1.4553... Generator Loss: 0.5295
Epoch 5/5... Discriminator Loss: 1.3456... Generator Loss: 0.5183
Epoch 5/5... Discriminator Loss: 1.6508... Generator Loss: 0.4109
Epoch 5/5... Discriminator Loss: 1.5997... Generator Loss: 0.3783
Epoch 5/5... Discriminator Loss: 1.6528... Generator Loss: 0.4058
Epoch 5/5... Discriminator Loss: 1.6057... Generator Loss: 0.3736
Epoch 5/5... Discriminator Loss: 1.4278... Generator Loss: 0.4827
Epoch 5/5... Discriminator Loss: 1.6146... Generator Loss: 0.4051
Epoch 5/5... Discriminator Loss: 1.7945... Generator Loss: 0.3131
Epoch 5/5... Discriminator Loss: 1.4855... Generator Loss: 0.5654
Epoch 5/5... Discriminator Loss: 1.6727... Generator Loss: 0.3589
Epoch 5/5... Discriminator Loss: 1.5363... Generator Loss: 0.4437
Epoch 5/5... Discriminator Loss: 1.5921... Generator Loss: 0.3863
Epoch 5/5... Discriminator Loss: 1.5369... Generator Loss: 0.4797
Epoch 5/5... Discriminator Loss: 1.7529... Generator Loss: 0.3320
Epoch 5/5... Discriminator Loss: 1.8258... Generator Loss: 0.3248
Epoch 5/5... Discriminator Loss: 1.4044... Generator Loss: 0.6266
Epoch 5/5... Discriminator Loss: 1.3579... Generator Loss: 0.6103
Epoch 5/5... Discriminator Loss: 1.8108... Generator Loss: 0.2851
Epoch 5/5... Discriminator Loss: 1.5258... Generator Loss: 0.4224
Epoch 5/5... Discriminator Loss: 1.4159... Generator Loss: 0.5353
Epoch 5/5... Discriminator Loss: 1.7026... Generator Loss: 0.3444
Epoch 5/5... Discriminator Loss: 1.4093... Generator Loss: 0.4562
Epoch 5/5... Discriminator Loss: 1.5534... Generator Loss: 0.4471
Epoch 5/5... Discriminator Loss: 1.0722... Generator Loss: 0.6910
Epoch 5/5... Discriminator Loss: 0.8471... Generator Loss: 1.1678
Epoch 5/5... Discriminator Loss: 1.4670... Generator Loss: 0.5890
Epoch 5/5... Discriminator Loss: 2.0597... Generator Loss: 0.2890
Epoch 5/5... Discriminator Loss: 1.9141... Generator Loss: 0.3073
Epoch 5/5... Discriminator Loss: 1.1502... Generator Loss: 0.7407
Epoch 5/5... Discriminator Loss: 1.5506... Generator Loss: 0.3703
Epoch 5/5... Discriminator Loss: 1.5337... Generator Loss: 0.3691
Epoch 5/5... Discriminator Loss: 1.8067... Generator Loss: 0.2990
Epoch 5/5... Discriminator Loss: 1.5031... Generator Loss: 0.4630
Epoch 5/5... Discriminator Loss: 1.3875... Generator Loss: 0.4748
Epoch 5/5... Discriminator Loss: 1.6915... Generator Loss: 0.3781
Epoch 5/5... Discriminator Loss: 1.5612... Generator Loss: 0.4225
Epoch 5/5... Discriminator Loss: 1.6785... Generator Loss: 0.3543
Epoch 5/5... Discriminator Loss: 1.4818... Generator Loss: 0.4568
Epoch 5/5... Discriminator Loss: 1.4855... Generator Loss: 0.4734
Epoch 5/5... Discriminator Loss: 1.5924... Generator Loss: 0.3772
Epoch 5/5... Discriminator Loss: 1.9279... Generator Loss: 0.2580
Epoch 5/5... Discriminator Loss: 1.6054... Generator Loss: 0.3955
Epoch 5/5... Discriminator Loss: 1.6668... Generator Loss: 0.3736
Epoch 5/5... Discriminator Loss: 1.4035... Generator Loss: 0.4574
Epoch 5/5... Discriminator Loss: 1.5953... Generator Loss: 0.3985
Epoch 5/5... Discriminator Loss: 1.6587... Generator Loss: 0.3932
Epoch 5/5... Discriminator Loss: 1.4836... Generator Loss: 0.4571
Epoch 5/5... Discriminator Loss: 1.5668... Generator Loss: 0.4274
Epoch 5/5... Discriminator Loss: 1.6976... Generator Loss: 0.3621
Epoch 5/5... Discriminator Loss: 1.7190... Generator Loss: 0.3537
Epoch 5/5... Discriminator Loss: 1.5295... Generator Loss: 0.4142
Epoch 5/5... Discriminator Loss: 1.9435... Generator Loss: 0.2671
Epoch 5/5... Discriminator Loss: 1.6791... Generator Loss: 0.4092
Epoch 5/5... Discriminator Loss: 1.7429... Generator Loss: 0.3306
Epoch 5/5... Discriminator Loss: 2.0286... Generator Loss: 0.2414
Epoch 5/5... Discriminator Loss: 1.3837... Generator Loss: 0.5109
Epoch 5/5... Discriminator Loss: 1.3794... Generator Loss: 0.4946
Epoch 5/5... Discriminator Loss: 1.1815... Generator Loss: 0.5879
Epoch 5/5... Discriminator Loss: 1.4809... Generator Loss: 0.4007

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

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